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The paper introduces DTAPP-IICR, a Delivery-Time Aware Prioritized Planning method with Incremental and Iterative Conflict Resolution, for preflight planning of large UAV fleets in dynamic airspaces with temporal No-Fly Zones and heterogeneous vehicle profiles. DTAPP-IICR uses a novel 4D single-agent planner (SFIPP-ST) to generate roundtrip trajectories while enforcing temporal NFZs and modeling inter-agent conflicts as soft constraints, followed by a Large Neighborhood Search guided by a geometric conflict graph. Experiments on benchmarks with up to 1,000 UAVs demonstrate near-100% success and up to 50% runtime reduction compared to batch Enhanced Conflict-Based Search, showcasing its scalability and practicality for dense urban airspace.
Achieve near-100% success rates planning routes for 1,000-UAV fleets in dynamic urban airspace with temporal no-fly zones, using a new prioritized planning method that outperforms existing approaches.
Preflight planning for large-scale Unmanned Aerial Vehicle (UAV) fleets in dynamic, shared airspace presents significant challenges, including temporal No-Fly Zones (NFZs), heterogeneous vehicle profiles, and strict delivery deadlines. While Multi-Agent Path Finding (MAPF) provides a formal framework, existing methods often lack the scalability and flexibility required for real-world Unmanned Traffic Management (UTM). We propose DTAPP-IICR: a Delivery-Time Aware Prioritized Planning method with Incremental and Iterative Conflict Resolution. Our framework first generates an initial solution by prioritizing missions based on urgency. Secondly, it computes roundtrip trajectories using SFIPP-ST, a novel 4D single-agent planner (Safe Flight Interval Path Planning with Soft and Temporal Constraints). SFIPP-ST handles heterogeneous UAVs, strictly enforces temporal NFZs, and models inter-agent conflicts as soft constraints. Subsequently, an iterative Large Neighborhood Search, guided by a geometric conflict graph, efficiently resolves any residual conflicts. A completeness-preserving directional pruning technique further accelerates the 3D search. On benchmarks with temporal NFZs, DTAPP-IICR achieves near-100% success with fleets of up to 1,000 UAVs and gains up to 50% runtime reduction from pruning, outperforming batch Enhanced Conflict-Based Search in the UTM context. Scaling successfully in realistic city-scale operations where other priority-based methods fail even at moderate deployments, DTAPP-IICR is positioned as a practical and scalable solution for preflight planning in dense, dynamic urban airspace.
